论文标题
基于学习方法的云服务器合并中主机过载检测和虚拟机选择的组合
A Combination of Host Overloading Detection and Virtual Machine Selection in Cloud Server Consolidation based on Learning Method
论文作者
论文摘要
在云数据中心（CDC）中，在保持性能的同时减少能耗一直是一个热门问题。在服务器合并中，传统解决方案是将问题分为多个小问题，例如主机过载检测，虚拟机（VM）选择和VM放置，并逐步解决它们。但是，主机超载检测策略和VM选择策略的设计不能直接与减少能耗和确保性能的最终目标有关。本文提出了一种基于学习的VM选择策略，该策略可在没有直接主机过载检测的情况下选择适当的迁移VM。从而减少了SLAV的产生，从而确保了性能并降低CDC的能耗。由实际VM工作负载轨迹驱动的模拟表明，我们的方法在减少SLAV的产生和CDC能源消耗方面优于现有方法。
In cloud data center (CDC), reducing energy consumption while maintaining performance has always been a hot issue. In server consolidation, the traditional solution is to divide the problem into multiple small problems such as host overloading detection, virtual machine (VM) selection and VM placement and solve them step by step. However, the design of host overloading detection strategies and VM selection strategies cannot be directly linked to the ultimate goal of reducing energy consumption and ensuring performance. This paper proposes a learning-based VM selection strategy that selects appropriate VMs for migration without direct host overloading detection. Thereby reducing the generation of SLAV, ensuring the performance and reducing the energy consumption of CDC. Simulations driven by real VM workload traces show that our method outperforms the existing methods in reducing SLAV generation and CDC energy consumption.
